Conjunctival microflora and its antibiotic sensitivity after serial intravitreal injections

Budzinskaya, M.V.; Strakhovskaya, M. G.; Andreeva, I.V.; Khalatyan, A.S.

doi:10.17116/oftalma2019135052135

Vestn. oftal'mol.

2019

DOI: 10.17116/oftalma2019135052135

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Conjunctival microflora and its antibiotic sensitivity after serial intravitreal injections

M.V. Budzinskaya

M. G. Strakhovskaya

I.V. Andreeva

et al.

Abstract: Введение. Тактика ведения пациентов с интравитреальными инъекциями в послеоперационном периоде требует единого подхода. Частое применение капель антибиотиков после инъекций приводит к возникновению резистентных штаммов конъюнктивы. Цель исследования-изучить состав флоры конъюнктивы и ее чувствительность к антибиотикам у пациентов, получивших 20 и более интравитреальных инъекций и сопутствующую антибиотикотерапию, и у 20 человек, сопоставимых по возрасту, без интравитреальных инъекций и офтальмологических опера… Show more

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Cited by 6 publications

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Predicting human microbe–drug associations via graph convolutional network with conditional random field

Long

Kwoh

et al. 2020

Bioinformatics

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Motivation Human microbes play critical roles in drug development and precision medicine. How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays. Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing. Considering the high cost and risk of biological experiments, the computational approach is an alternative choice. However, at present, few computational approaches have been developed to tackle this task. Results In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity network, and a microbe-drug interaction network. We then proposed a novel Graph Convolutional Network (GCN) based framework for predicting human Microbe-Drug Associations, named GCNMDA. In the hidden layer of GCN, we further exploited the Conditional Random Field (CRF), which can ensure that similar nodes (i.e., microbes or drugs) have similar representations. To more accurately aggregate representations of neighborhoods, an attention mechanism was designed in the CRF layer. Moreover, we performed a random walk with restart (RWR) based scheme on both drug and microbe similarity networks to learn valuable features for drugs and microbes respectively. Experimental results on three different datasets showed that our GCNMDA model consistently achieved better performance than seven state-of-the-art methods. Case studies for three microbes including SARS-CoV-2 and two antimicrobial drugs (i.e., Ciprofloxacin and Moxifloxacin) further confirmed the effectiveness of GCNMDA in identifying potential microbe-drug associations. Availability Python codes and dataset are available at: https://github.com/longyahui/GCNMDA. Supplementary information Supplementary data are available at Bioinformatics online.

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Predicting human microbe–drug associations via graph convolutional network with conditional random field

Long

Kwoh

et al. 2020

Bioinformatics

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Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction

Xuan,

Xu,

Cui

et al. 2024

Bioinformatics

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Motivation The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively. Results We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on 3 drugs further showed DHDMP’s potential application in discovering the reliable candidate microbes for the interested drugs. Availability Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDM. Supplementary information Supplementary data are available at Bioinformatics online.

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Ophthalmic safety profile of antiangiogenic therapy

et al. 2021

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Conjunctival microflora and its antibiotic sensitivity after serial intravitreal injections

Cited by 6 publications

References 6 publications

Predicting human microbe–drug associations via graph convolutional network with conditional random field

Predicting human microbe–drug associations via graph convolutional network with conditional random field

Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction

Ophthalmic safety profile of antiangiogenic therapy

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